Pattern extraction apparatus

ABSTRACT

Collation Fourier image data (FIG.  1 D) FB generated by performing the two-dimensional discrete Fourier transform (DFT) for the image data (FIG.  1 C) of a collation pattern is synthesized with registration Fourier image data (FIG.  1 B) generated by performing the two-dimensional DFT for the image data of a registration pattern. After amplitude suppression processing is performed for the resultant data, two-dimensional DFT is performed. A correlation peak is extracted from a correlation component area which appears in the synthesized Fourier image data (FIG.  1 E) for which the two-dimensional DFT has been performed. A predetermined area including this correlation peak is then masked (FIG.  1 F). The two-dimensional DFT is performed for the masked synthesized Fourier image data, and amplitude restoration processing is performed for the data. The resultant data is re-synthesized with the registration Fourier image data FA, and the two-dimensional IDFT is performed for the synthesized data. With this processing, image data containing a difference pattern like the one shown in FIG.  1 G or a moving pattern can be obtained.

TECHNICAL FIELD

The present invention relates to a pattern extraction apparatus forextracting moving patterns and differences between registration andcollation patterns by collating N-dimensional patterns (e.g., voicepatterns (one-dimensional), plane image patterns (two-dimensional), andstereoscopic patterns (three-dimensional) on the basis of spatialfrequency characteristics.

BACKGROUND ART

Conventionally, a difference between two similar patterns has beenextracted by a human visual check. More specifically, one of thepatterns is set as a reference pattern, and the reference pattern andthe other pattern are compared through the human eye, thereby extractinga difference.

In addition, common patterns (moving patterns) that are present atdifferent positions in two similar patterns (overall patterns) are alsoextracted by a human visual check. More specifically, one of thepatterns is set as a reference pattern, and the reference pattern andthe other pattern are compared through the human eye, thereby extractingmoving patterns.

A human visual check, however, can cope with only a case wherein adifference or moving pattern between two similar pattern is clear. Thatis, if overall patterns are complicated or a difference or movingpattern is small, it takes time to extract it, and an accurate checkcannot be made. If a plurality of moving patterns are present, it isdifficult to detect all the moving patterns.

It is an object of the present invention to provide a pattern extractionapparatus which can accurately extract a difference and moving patternbetween similar patterns in a short period of time.

DISCLOSURE OF INVENTION

According to the present invention, registration Fourier N-dimensionalpattern data is generated by performing N-dimensional discrete Fouriertransform for N-dimensional pattern data of a registration pattern,collation Fourier N-dimensional pattern data is generated by performingN-dimensional discrete Fourier transform for N-dimensional pattern dataof a collation pattern, one of the N-dimensional discrete Fouriertransform and N-dimensional inverse discrete Fourier transform isperformed in a first pattern processing means for synthesized FourierN-dimensional pattern data obtained by synthesizing the registrationFourier N-dimensional pattern data and the collation FourierN-dimensional pattern data, a correlation peak in a correlationcomponent area appearing in the synthesized Fourier N-dimensionalpattern data for which the Fourier transform has been performed isobtained, a portion around the obtained correlation peak is masked, theN-dimensional inverse discrete Fourier transform is performed for thesynthesized Fourier N-dimensional pattern data in which the portion ismasked when the N-dimensional discrete Fourier transform is performed inthe first pattern processing means, the N-dimensional discrete Fouriertransform is performed for the pattern data when the N-dimensionalinverse discrete Fourier transform is performed in the first patternprocessing means, and the N-dimensional inverse discrete Fouriertransform is performed for re-synthesized Fourier N-dimensional patterndata generated by re-synthesizing the synthesized Fourier N-dimensionalpattern data for which the Fourier transform has been performed and theregistration Fourier N-dimensional pattern data.

According to the present invention, registration Fourier N-dimensionalpattern data is generated by performing N-dimensional discrete Fouriertransform for N-dimensional pattern data of a registration pattern, andcollation Fourier N-dimensional pattern data is generated by performingN-dimensional discrete Fourier transform for N-dimensional pattern dataof a collation pattern. The N-dimensional discrete Fourier transform orN-dimensional inverse discrete Fourier transform is performed in thefirst pattern processing means for synthesized Fourier N-dimensionalpattern data obtained by synthesizing the registration FourierN-dimensional pattern data and the collation Fourier N-dimensionalpattern data. A correlation peak in a correlation component areaappearing in the synthesized Fourier N-dimensional pattern data forwhich the Fourier transform has been performed is obtained. A portionaround the obtained correlation peak is masked. The N-dimensionalinverse discrete Fourier transform is performed for the synthesizedFourier N-dimensional pattern data in which the portion is masked whenthe N-dimensional discrete Fourier transform is performed in the firstpattern processing means. The N-dimensional discrete Fourier transformis performed for the pattern data when the N-dimensional inversediscrete Fourier transform is performed in the first pattern processingmeans. The N-dimensional inverse discrete Fourier transform is performedfor re-synthesized Fourier N-dimensional pattern data generated byre-synthesizing the synthesized Fourier N-dimensional pattern data forwhich the Fourier transform has been performed and the registrationFourier N-dimensional pattern data. The contour of a difference ormoving pattern is extracted from the re-synthesized FourierN-dimensional pattern data having undergone the inverse discrete Fouriertransform. The location of the difference or moving pattern can be knownwhatsoever.

According to the present invention, registration Fourier N-dimensionalpattern data and collation Fourier N-dimensional pattern data aresynthesized, and amplitude suppression processing (log processing, rootprocessing, or the like) is performed for the resultant synthesizedFourier N-dimensional pattern data. One of the N-dimensional discreteFourier transform or N-dimensional inverse discrete Fourier transform isperformed for the data, and amplitude restoration processing (inversefunction processing of log processing, root processing, or the like) isperformed for the synthesized Fourier N-dimensional pattern data forwhich the Fourier transform has been performed by the second patternprocessing means. The synthesized Fourier N-dimensional pattern datahaving undergone the amplitude restoration processing and theregistration Fourier N-dimensional pattern data are re-synthesized.N-dimensional inverse discrete Fourier transform is performed for theresultant re-synthesized Fourier N-dimensional pattern data.

In addition, according to the present invention, registration FourierN-dimensional pattern data and collation Fourier N-dimensional patterndata are synthesized, and amplitude suppression processing (logprocessing, root processing, or the like) is performed for the resultantsynthesized Fourier N-dimensional pattern data. One of the N-dimensionaldiscrete Fourier transform or N-dimensional inverse discrete Fouriertransform is performed for the data. The synthesized FourierN-dimensional pattern data for which the Fourier transform has beenperformed by the second pattern processing means and the registrationFourier N-dimensional pattern data are re-synthesized. N-dimensionalinverse discrete Fourier transform is performed for the resultantre-synthesized Fourier N-dimensional pattern data.

Furthermore, according to the present invention, registration FourierN-dimensional pattern data is generated by performing amplitudesuppression processing (log processing, root processing, or the like)for the N-dimensional pattern data of a registration pattern afterperforming N-dimensional discrete Fourier transform is performed for thepattern data. Collation Fourier N-dimensional pattern data is generatedby performing amplitude suppression processing (log processing, rootprocessing, or the like) for the N-dimensional pattern data of acollation pattern after performing N-dimensional discrete Fouriertransform for the pattern data. The synthesized Fourier N-dimensionalpattern data for which the Fourier transform has been performed by thesecond pattern processing means and the registration FourierN-dimensional pattern data are re-synthesized. The N-dimensional inversediscrete Fourier transform is performed for the resultant re-synthesizedFourier N-dimensional pattern data after amplitude restorationprocessing (inverse function processing of log processing, rootprocessing, or the like) is performed for the pattern data.

Moreover, according to the present invention, registration FourierN-dimensional pattern data is generated by performing amplitudesuppression processing (log processing, root processing, or the like)for the N-dimensional pattern data of a registration pattern afterperforming N-dimensional inverse discrete Fourier transform is performedfor the pattern data. Collation Fourier N-dimensional pattern data isgenerated by performing amplitude suppression processing (logprocessing, root processing, or the like) for the N-dimensional patterndata of a collation pattern after performing N-dimensional discreteFourier transform for the pattern data. The synthesized FourierN-dimensional pattern data for which the Fourier transform has beenperformed by the second pattern processing means and the registrationFourier N-dimensional pattern data are re-synthesized. The N-dimensionalinverse discrete Fourier transform is performed for the resultantre-synthesized Fourier N-dimensional pattern data.

BRIEF DESCRIPTION OF DRAWINGS

FIGS. 1A to 1G are views for explaining the steps in extracting adifference between a registration pattern and a collation pattern in apattern extraction apparatus in FIG. 2;

FIG. 2 is a block diagram of the pattern extraction apparatus accordingto an embodiment of the present invention;

FIG. 3 is a flow chart for explaining reference (registration) patternregistration operation in the pattern extraction apparatus in FIG. 2;

FIG. 4 is a flow chart for explaining extraction of the differencebetween the registration pattern and the collation pattern in thepattern extraction apparatus in FIG. 2;

FIG. 5 is a flow chart for explaining extraction of the differencebetween the registration pattern and the collation pattern, followingthe flow chart of FIG. 4;

FIGS. 6A to 6G are views for explaining the steps in extracting a movingpattern in the pattern extraction apparatus in FIG. 2;

FIG. 7 is a flow chart for explaining moving pattern extractionoperation in the pattern extraction apparatus in FIG. 2;

FIG. 8 is a flow chart for explaining the moving pattern extractionoperation, following the flow chart of FIG. 7;

FIG. 9 is a functional block diagram of a pattern extraction algorithmcorresponding to the flow charts of FIGS. 4 and 5; and

FIG. 10 is a functional block diagram of a pattern extraction algorithmcorresponding to the flow charts of FIGS. 7 and 8.

BEST MODE OF CARRYING OUT THE INVENTION

The present invention will be described in detail below with referenceto the present invention.

FIG. 2 is a block diagram showing a pattern extraction apparatusaccording to an embodiment of the present invention. FIG. 2 explains acase wherein two-dimensional pattern data consisting of image data arecollated. Referring to FIG. 2, reference numeral 10 denotes an operationsection; and 20, a control section. The operation section 10 includes aten-key pad 10-1, a display (LCD: Liquid Crystal Display) 10-2 and a CCD(Charge Coupled Device) camera 10-3. The control section 20 is comprisedof a control section 20-1 including a CPU (Central Processing Unit), aROM (Read Only Memory) 20-2, a RAM (Random Access Memory) 20-3, a harddisk (HD) 20-4, a frame memory (FM) 20-5, an external connection section(I/F) 20-6, and a Fourier transform (FFT) section 20-7. A patternextraction program is stored in the ROM 20-2.

[Registration of Reference Pattern]

In this pattern extraction apparatus, a reference pattern (registrationpattern) is registered in the manner shown in FIG. 3. Before patterncollation, the user inputs the ID number assigned to the referencepattern with the ten-key pad 10-1 (step 301), and places theregistration pattern at a predetermined position in the visual fieldrange of the CCD camera 10-3. With this operation, the original image ofthe registration pattern is A/D-converted into a 256-level halftoneimage (image data: two-dimensional pattern data) constituted by 320×400pixels. The resultant data is supplied to the control section 20.

The control section 20-1 loads the registration pattern image datasupplied from the operation section 10 through the frame memory 20-5(step 302), and sends the loaded registration pattern image data (seeFIG. 1A) to the Fourier transform section 20-7 to performtwo-dimensional discrete Fourier transform (DFT) for the image data(step 304). With this operation, the registration pattern image datashown in FIG. 1A becomes Fourier image data (registration Fourier imagedata) FA shown in FIG. 1B. The control section 20-1 files this Fourierimage data FA as the original image data of the registration pattern incorrespondence with the ID number input to the hard disk 20-4 (step305).

For example, the two-dimensional discrete Fourier transform is describedin “Introduction to Computer Image Processing”, edited by Nihon KogyoGijutu Center, pp. 44 to 45 (reference 1) and the like.

[Extraction of Difference]

In this pattern extraction apparatus, a difference between aregistration pattern and a collation pattern is extracted in the mannershown in FIG. 4. The user uses the ten-key pad 10-1 to input the IDnumber assigned to a reference pattern (step 401), and places thecollation pattern at a predetermined position in the visual field rangeof the ten-key pad 10-1. With this operation, as in the case with theregistration pattern, the original image data of the collation patternis supplied as 320×400-pixel, 256-level halftone image (image data:two-dimensional pattern data) to the control section 20.

Upon reception of the ID number through the ten-key pad 10-1, thecontrol section 20-1 reads out the registration pattern Fourier imagedata FA corresponding to the ID number from the registration patternsfiled in the hard disk 20-4 (step 402). The control section 20-1 loadsthe collation pattern image data supplied from the operation section 10through the frame memory 20-5 (step 403), and sends the loaded collationpattern image data (see FIG. 1C) to the Fourier transform section 20-7to perform two-dimensional discrete Fourier transform (two-dimensionalDFT) for the collation pattern image data (step 405). With thisoperation, the collation pattern image data shown in FIG. 1C becomesFourier image data (collation Fourier image data) FB shown in FIG. 1D.

The control section 20-1 synthesizes the collation pattern Fourier imagedata obtained in step 405 with the registration pattern Fourier imagedata read out in step 402 to obtain synthesized Fourier image data (stepS406).

Letting A·e^(j)θ be the collation Fourier image data, and B·e^(j)φ bethe registration Fourier image data, this synthesized Fourier image datais represented by A·B·e^(j(θ−φ)). Note that A, B, θ, and φ are thefunctions of a frequency (Fourier) space (u, v).

A·B·e^(j(θ−φ)) is rewritten as

A·B·e^(j(θ−φ))=A·B·cos (θ−φ)+j·A·B·sin (θ−φ)  (1)

If A·e^(j)θ=α₁+jβ₁ and B·e^(j)φ=α₂+j_(β) ₂, then

A=(α₁ ²+β₁ ²)^(½)

B=(α₂ ²+β₂ ²)^(½)

θ=tan⁻¹(β₁/α₁)

φ=tan⁻¹(β₂/α₂)

By calculating equation (1), synthesized Fourier image data is obtained.

Note that synthesized Fourier image data may be obtained by equation(2): $\begin{matrix}\begin{matrix}{{A \cdot B \cdot {^{j}\left( {\theta - \varphi} \right)}} = \quad {{A \cdot B \cdot ^{j}}{\theta \cdot ^{- j}}\varphi}} \\{= \quad {{A \cdot ^{j}}{\theta \cdot B \cdot ^{- j}}\varphi}} \\{= \quad {\left( {\alpha_{1} + {j\quad \beta_{1}}} \right) \cdot \left( {\alpha_{2} - {j\quad \beta_{2}}} \right)}} \\{= \quad {\left( {{\alpha_{1} \cdot \alpha_{2}} + {\beta_{1} \cdot \beta_{2}}} \right) +}} \\{\quad {{j\left( {{\alpha_{2} \cdot \beta_{1}} - {\alpha_{1} \cdot \beta_{2}}} \right)}.}}\end{matrix} & (2)\end{matrix}$

After the synthesized Fourier image data is obtained in this manner, thecontrol section 20-1 performs amplitude suppression processing for theimage data by using phase only correlation (step 407). In thisembodiment, log processing is performed as amplitude suppressionprocessing. More specifically, the log of A·B·e^(j(θ−φ)), which is themathematical expression of the above synthesized Fourier image data, iscalculated as log(A·B)·e^(j(θ−φ)), thereby suppressing A·B representingthe amplitude to log(A·B) (A·B>log(A·B)). The above phase onlycorrelation is cross correlation that is used for correction inconsideration of the spatial phase change of an image. The amplitudeinformation is suppressed by this method to obtain synthesized Fourierimage data limited to only the phase information.

The synthesized Fourier image data having undergone amplitudesuppression processing is less susceptible to the illuminance differencebetween the case in which the registration pattern is obtained and thecase in which the collation pattern is obtained. That is, by performingamplitude suppression processing, the spectrum intensity of each pixelis suppressed to cut extreme values. As a result, more information ismade effective.

In this embodiment, log processing is performed as amplitude suppressionprocessing. However, root processing may be performed. In addition, anytype of processing, other than log processing and root processing, maybe performed as long as amplitudes can be suppressed. If, for example,all amplitudes are set to 1 in amplitude suppression processing, i.e.,only phase data are to be processed, both the computation amount and theamount of data processed can be reduced as compared with log processing,root processing, and the like.

Upon performing amplitude suppression processing in step 407, thecontrol section 20-1 sends the synthesized Fourier image data havingundergone the amplitude suppression processing to the Fourier transformsection 20-7 to perform the second two-dimensional DFT (step 408). Withthis operation, the synthesized Fourier image data having undergone theamplitude suppression processing becomes synthesized Fourier image datalike the one shown in FIG. 1E.

The control section 20-1 loads the synthesized Fourier image dataobtained in step S408, and scans the intensities (amplitudes) of thecorrelation components of the respective pixels in a predeterminedcorrelation component area including a central portion from thissynthesized Fourier image data to obtain the histogram of theintensities of the correlation components of the respective pixels. Thecontrol section 20-1 then extracts a pixel having the highest intensity(correlation peak) in the correlation component area from this histogram(step S409). In this case, the correlation peak appears near the centerof the correlation component area.

The control section 20-1 masks a portion around the correlation peakextracted in step S409 (step S410). More specifically, as shown in FIG.1F, the control section 20-1 masks an area S0 enclosed with a whitedotted line on the synthesized Fourier image data shown in FIG. 1E. Thetwo-dimensional inverse discrete Fourier transform (two-dimensionalIDFT) is performed for the synthesized Fourier image data having thearea S0 masked (step S411), and amplitude restoration processing isperformed for the synthesized Fourier image data having undergone thetwo-dimensional IDFT (step S412). In this case, in the amplituderestoration processing, the inverse function of the function executed inthe amplitude suppression processing in step S407 is performed for theamplitude. A² is set for {square root over (A)}; and e^(A), forlog_(e)A.

The control section 20-1 re-synthesizes the synthesized Fourier imagedata having undergone the amplitude restoration processing in step S412and the registration Fourier image data FA read out in step S402 (stepS413), and performs the two-dimensional IDFT for the resultantre-synthesized Fourier image data (step S414), thereby obtainingre-synthesized Fourier image data like the one shown in FIG. 1G.

To perform re-synthesis in step S413 is to extract a collation pattern Afrom the pattern obtained by synthesizing a registration pattern B andthe collation pattern A. More specifically, re-synthesis can beperformed in the following two ways. These two ways are equivalent toeach other.

{circle around (1)} If the synthesis in step S406 is expressed byA·B·e^(j(θ−φ)), the re-synthesis in step S413 is represented by(A·B)/B·e^(j((θ−φ)+φ))=A·e^(j)θ.

{circle around (2)} If the synthesis in step S406 is expressed byA·B·e^(j(φ−θ)), the re-synthesis in step S413 is represented by(A·B)/B·e^(j((φ−(θ−φ)))=A·e^(j)θ.

As is obvious from FIG. 1G, in the re-synthesized Fourier image datahaving undergone this two-dimensional IDFT, the contour of the patternthat is present in only the collation pattern appears at thecorresponding position. More specifically, in the collation patternshown in FIG. 1C, a car pattern is superimposed on a portion. In FIG.1G, this car pattern appears as a difference between the collationpattern and the registration pattern. The control section 20-1 extractsthe car pattern appearing in FIG. 1G as a pattern that is present inonly the collation pattern (step S415).

[Extraction of Moving Pattern]

In the above case of extraction of the difference between theregistration pattern and the collation pattern, the car pattern issuperimposed on only the collation pattern but no car pattern issuperimposed on the registration pattern. In contrast to this, assumethat a car pattern is superimposed on both a registration pattern and acollation pattern, and the position of this car pattern is moving.

In this case as well, the control section 20-1 performs processingsimilar to that shown in the flow charts of FIGS. 4 and 5, and extractsa moving pattern through the steps shown in FIG. 6. In this case, acorrelation value P1 representing a background and a correlation valueP2 representing a car appear in FIG. 6E showing the step correspondingto that shown in FIG. 1E. In this case, the correlation value P1 islarger and extracted as a correlation peak, and hence a portion aroundthe correlation value P1 is masked (see FIG. 6F).

The control section 20-1 performs the two-dimensional IDFT for themasked synthesized Fourier image data (step S411), and performsamplitude restoration processing for the synthesized Fourier image datahaving undergone the two-dimensional IDFT (step S412). The controlsection 20-1 then re-synthesizes the synthesized Fourier image datahaving undergone this amplitude restoration processing and theregistration Fourier image data FA read out in step S402 (step S413),and performs the two-dimensional IDFT for the resultant re-synthesizedFourier image data (step S414), thereby obtaining re-synthesized Fourierimage data like the one shown in FIG. 6G.

As is obvious from FIG. 6G, in the re-synthesized Fourier image datahaving undergone the two-dimensional IDFT, the contour of the movingpattern that is present in both the registration pattern and thecollation pattern appears at the position corresponding to that in thecollation pattern. That is, in the collation pattern in FIG. 6C, theposition of the car in the registration pattern in FIG. 6A has moved. InFIG. 6G, this car pattern appears as a moving pattern that is present atdifferent positions in the collation pattern and the registrationpattern. The control section 20-1 extracts the car pattern appearing inFIG. 6G as a moving pattern that is present at different positions inthe registration pattern and the collation pattern (step S416).

In this embodiment, the two-dimensional IDFT is performed by the Fouriertransform section 20-7. However, this processing may be performed in theCPU 20-1. In addition, in this embodiment, the two-dimensional DFT isperformed in step S408 in FIG. 4. However, the two-dimensional IDFT maybe performed instead of the two-dimensional DFT. That is, thetwo-dimensional IDFT may be performed for synthesized Fourier image datahaving undergone amplitude suppression processing instead oftwo-dimensional DFT. If, however, the two-dimensional IDFT is performedin step S408, the two-dimensional DFT is performed in step S411.Quantitatively, no change in collation precision occurs whethertwo-dimensional discrete Fourier transform or two-dimensional discreteinverse Fourier transform is performed. The two-dimensional IDFT isdescribed in reference 1.

In this embodiment, amplitude suppression processing is performed firstfor synthesized Fourier image data, and the two-dimensional DFT is thenperformed in step S408. However, amplitude suppression processing may beperformed for the registration Fourier image data FA and the collationFourier image data FB before synthesis, and the resultant data may besynthesized. More specifically, as shown in FIG. 7, step S407 in FIG. 4is omitted, and step S703 of performing amplitude suppression processingis set between step S702 of reading out the registration Fourier imagedata FA and step S704 of inputting the collation pattern image data. Inaddition, step S707 of performing amplitude suppression processing isset between step S706 of performing two-dimensional DFT for thecollation pattern image data and step S708 of synthesizing the collationFourier image data FB having undergone amplitude suppression processingand the registration Fourier image data FA. In this case, however, asshown in FIG. 8, step S714 of performing amplitude restorationprocessing is set after step S713 of re-synthesizing the synthesizedFourier image data having undergone the two-dimensional IDFT and theregistration Fourier image data read out in step S702.

In this case, the registration Fourier image data and collation Fourierimage data that have undergone amplitude suppression processing can beobtained by the amplitude suppression processing in steps S703 and S707,and the synthesized Fourier image data is obtained by synthesizing theseFourier image data. Note that steps S701, S709 to S712, and S717 to S717are the same as steps S401, S409 to S412, and S417 to S417 in FIGS. 4and 5, and a description thereof will be omitted.

The suppression ratio of the amplitude of the synthesized Fourier imagedata in this case is low as compared with the case shown in FIGS. 4 and5 in which amplitude suppression processing is performed aftersynthesized Fourier image data is generated. Therefore, the method ofperforming amplitude suppression processing after synthesized Fourierimage data is generated as shown in FIG. 4 is superior in collationprecision to the method of generating synthesized Fourier image dataafter performing amplitude suppression processing as shown in FIG. 7. Inthe case shown in FIGS. 7 and 8 as well, in which synthesized Fourierimage data is generated after amplitude suppression processing isperformed, the two-dimensional IDFT may be performed for the synthesizedFourier image data instead of the two-dimensional DFT.

Furthermore, in the embodiment described above, two-dimensional patternextraction processing has been described. However, three-dimensionalpattern extraction processing can be performed in the same manner asdescribed above, and extraction processing of multidimensional patterns,other than two- and three-dimensional patterns, can be performed in thesame manner as described above. Furthermore, in the above embodiment,amplitude suppression processing is performed. However, amplitudesuppression processing need not always be performed. If, for example,all amplitudes are set to 1 in amplitude suppression processing, i.e.,phase only correlation is performed, amplitude restoration processingneed not be performed.

FIG. 9 is a functional block diagram of a pattern extraction algorithmcorresponding to the flow charts shown in FIGS. 4 and 5. FIG. 10 is afunctional block diagram of a pattern extraction algorithm correspondingto the flow charts shown in FIGS. 7 and 8. Referring to FIGS. 9 and 10,the same step numbers as those in the flow charts are attached to therespective functional blocks, and the respective blocks have thefunctions of the steps corresponding to the attached step numbers.

As has been described above, according to the present invention, aregistration pattern and a collation pattern are collated with eachother on the basis of the spatial frequency characteristics, and adifference or moving pattern between the similar patterns can beextracted as the collation result. This allows accurate quality check,abnormality detection (analysis), and detection of a moving objectwithin a short period of time.

What is claimed is:
 1. A pattern extraction apparatus characterized bycomprising: registration Fourier pattern data generating means forgenerating registration Fourier N-dimensional pattern data by performingN-dimensional discrete Fourier transform for N-dimensional pattern dataof a registration pattern; collation Fourier pattern data generatingmeans for generating collation Fourier N-dimensional pattern data byperforming N-dimensional discrete Fourier transform for N-dimensionalpattern data of a collation pattern; first pattern processing means forperforming one of the N-dimensional discrete Fourier transform andN-dimensional inverse discrete Fourier transform for synthesized FourierN-dimensional pattern data obtained by synthesizing the registrationFourier N-dimensional pattern data generated by said registrationFourier pattern data generating means and the collation FourierN-dimensional pattern data generated by said collation Fourier patterndata generating means; mask processing means for obtaining a correlationpeak in a correlation component area appearing in the synthesizedFourier N-dimensional pattern data for which the Fourier transform hasbeen performed by said first pattern processing means, and masking apredetermined area including the obtained correlation peak; secondpattern processing means for performing the N-dimensional inversediscrete Fourier transform for the synthesized Fourier N-dimensionalpattern data in which the predetermined area is masked by said maskprocessing means when the N-dimensional discrete Fourier transform isperformed for the pattern data by said first pattern processing means,and performing the N-dimensional discrete Fourier transform for thepattern data when the N-dimensional inverse discrete Fourier transformis performed for the pattern data by said first pattern processingmeans; and third pattern processing means for performing theN-dimensional inverse discrete Fourier transform for re-synthesizedFourier N-dimensional pattern data generated by re-synthesizing thesynthesized Fourier N-dimensional pattern data for which the Fouriertransform has been performed by said second pattern processing means andthe registration Fourier N-dimensional pattern data generated by saidregistration Fourier pattern data generating means.
 2. A patternextraction apparatus according to claim 1, characterized in that saidfirst pattern processing means comprises: pattern data synthesizingmeans for synthesizing the registration Fourier N-dimensional patterndata generated by said registration Fourier pattern data generatingmeans and the collation Fourier N-dimensional pattern data generated bysaid collation Fourier pattern data generating means; amplitudesuppression processing means for performing amplitude suppressionprocessing for the synthesized Fourier N-dimensional pattern dataobtained by said pattern data synthesizing means; and first Fouriertransform means for performing one of the N-dimensional discrete Fouriertransform and the N-dimensional inverse discrete Fourier transform forthe synthesized Fourier N-dimensional pattern data for which amplitudesuppression has been performed by said amplitude suppression processingmeans, and said third pattern processing means comprises: amplituderestoration processing means for performing amplitude restorationprocessing for the synthesized Fourier N-dimensional pattern data forwhich Fourier transform has been performed by said second patternprocessing means; pattern data re-synthesizing means for re-synthesizingthe synthesized Fourier N-dimensional pattern data for which amplituderestoration has been performed by said amplitude suppression processingmeans and the registration Fourier N-dimensional pattern data generatedby said registration Fourier pattern data; and second Fourier transformmeans for performing N-dimensional inverse discrete Fourier transformfor the re-synthesized Fourier N-dimensional pattern data obtained bysaid pattern data re-synthesizing means.
 3. A pattern extractionapparatus according to claim 1, characterized in that said first patternprocessing means comprises: pattern data synthesizing means forsynthesizing the registration Fourier N-dimensional pattern datagenerated by said registration Fourier pattern data generating means andthe collation Fourier N-dimensional pattern data generated by saidcollation Fourier pattern data generating means; amplitude suppressionprocessing means for performing amplitude suppression processing for thesynthesized Fourier N-dimensional pattern data obtained by said patterndata synthesizing means; and first Fourier transform means forperforming one of the N-dimensional discrete Fourier transform and theN-dimensional inverse discrete Fourier transform for the synthesizedFourier N-dimensional pattern data for which amplitude suppression hasbeen performed by said amplitude suppression processing means, and saidthird pattern processing means comprises: pattern data re-synthesizingmeans for re-synthesizing the synthesized Fourier N-dimensional patterndata for which the Fourier transform has been performed by said secondpattern processing means and the registration Fourier N-dimensionalpattern data generated by said registration Fourier pattern datagenerating means; and second Fourier transform means for performingN-dimensional inverse discrete Fourier transform for the re-synthesizedFourier N-dimensional pattern data obtained by said pattern datare-synthesizing means.
 4. A pattern extraction apparatus according toclaim 1, characterized in that said registration Fourier pattern datagenerating means comprises: first Fourier transform means for performingN-dimensional discrete Fourier transform for N-dimensional pattern dataof a registration pattern; and first amplitude suppression processingmeans for generating registration Fourier N-dimensional pattern data byperforming amplitude suppression processing for the N-dimensionalpattern data of the registration pattern for which the Fourier transformhas been performed by said first Fourier transform means, said collationFourier pattern data generating means comprises: second Fouriertransform means for performing N-dimensional discrete Fourier transformfor the N-dimensional pattern data of the collation pattern; and secondamplitude suppression processing means for generating collation FourierN-dimensional pattern data by performing amplitude suppressionprocessing for the N-dimensional pattern data of the collation patternfor which the Fourier transform has been performed by said secondFourier transform means, and said third pattern processing meanscomprises: pattern data re-synthesizing means for re-synthesizing thesynthesized Fourier N-dimensional pattern data for which the Fouriertransform has been performed by said second pattern processing means andthe registration Fourier N-dimensional pattern data generated by saidregistration Fourier pattern data generating means; amplituderestoration processing means for performing amplitude restorationprocessing for the re-synthesized Fourier N-dimensional pattern dataobtained by said pattern data re-synthesizing means; and third Fouriertransform means for performing the N-dimensional inverse discreteFourier transform for the re-synthesized Fourier N-dimensional patterndata for which amplitude restoration has been performed by saidamplitude restoration processing means.
 5. A pattern extractionapparatus according to claim 1, characterized in that said registrationFourier pattern data generating means comprises: first Fourier transformmeans for performing N-dimensional discrete Fourier transform forN-dimensional pattern data of a registration pattern; and firstamplitude suppression processing means for generating registrationFourier N-dimensional pattern data by performing amplitude suppressionprocessing for the N-dimensional pattern data of the registrationpattern for which the Fourier transform has been performed by said firstFourier transform means, said collation Fourier pattern data generatingmeans comprises: second Fourier transform means for performingN-dimensional discrete Fourier transform for the N-dimensional patterndata of the collation pattern; and second amplitude suppressionprocessing means for generating collation Fourier N-dimensional patterndata by performing amplitude suppression processing for theN-dimensional pattern data of the collation pattern for which theFourier transform has been performed by said second Fourier transformmeans, and said third pattern processing means comprises: pattern datare-synthesizing means for re-synthesizing the synthesized FourierN-dimensional pattern data for which the Fourier transform has beenperformed by said second pattern processing means and the registrationFourier N-dimensional pattern data generated by said registrationFourier pattern data generating means; and third Fourier transform meansfor performing the N-dimensional inverse discrete Fourier transform forthe re-synthesized Fourier N-dimensional pattern data obtained by saidpattern data re-synthesizing means.
 6. A pattern extraction apparatusaccording to claim 1, characterized in that said mask processing meanscomprises: correlation peak calculation means for obtaining acorrelation peak in a correlation component area on the basis of ahistogram of intensities of correlation components of the synthesizedFourier N-dimensional pattern data for which the Fourier transform hasbeen performed by said first pattern processing means; and mask meansfor masking a predetermined area including the correlation peak obtainedby said correlation peak calculation means.
 7. A pattern extractionapparatus according to claim 1, characterized by further comprisingpattern extraction means for extracting a different pattern that ispresent in only the collation pattern from the re-synthesized FourierN-dimensional pattern data for which the inverse Fourier transform hasbeen performed by said third pattern processing means.
 8. A patternextraction apparatus according to claim 1, characterized by furthercomprising pattern extraction means for extracting a moving pattern thatis present at different positions in the collation pattern and theregistration pattern from the re-synthesized Fourier N-dimensionalpattern data for which the inverse Fourier transform has been performedby said third pattern processing means.